Introduces Ex-ToxiCN-MM dataset and RIKE framework (with AKE and RIR modules) that outperforms baselines on attributing harm in ambiguous Chinese memes using C-HarmKB.
Sarthak Roy, Ashish Harshvardhan, Animesh Mukher- jee, and Punyajoy Saha
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
LLMs can be statistically superior to humans at estimating group-level judgments on subjective tasks because of their low variance and decoupled representation-processing biases.
SMARTER boosts LLM toxicity detection and explanation performance by up to 13% macro-F1 on three hate-speech benchmarks through self-generated synthetic data and minimal-supervision preference optimization.
PaLM 2 reports state-of-the-art results on language, reasoning, and multilingual tasks with improved efficiency over PaLM.
citing papers explorer
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Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes
Introduces Ex-ToxiCN-MM dataset and RIKE framework (with AKE and RIR modules) that outperforms baselines on attributing harm in ambiguous Chinese memes using C-HarmKB.
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From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?
LLMs can be statistically superior to humans at estimating group-level judgments on subjective tasks because of their low variance and decoupled representation-processing biases.
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SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models
SMARTER boosts LLM toxicity detection and explanation performance by up to 13% macro-F1 on three hate-speech benchmarks through self-generated synthetic data and minimal-supervision preference optimization.
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PaLM 2 Technical Report
PaLM 2 reports state-of-the-art results on language, reasoning, and multilingual tasks with improved efficiency over PaLM.